#This script executes regressions to estimate 
#associations between first-generation prenatal exposure to nonattainment 
#and first-generation educational outcomes.

rm(list=ls())
gc()
library(reldist)
library(ineq)
library(stargazer)
library(data.table)
library(dplyr)
library(dtplyr)
library(ggplot2)
library(stringdist)
library(lfe)
library(haven)
library(readr)
library(broom)
library(tidylog)
library(rdrobust)

# Set the working directory
setwd("/projects/opp_env/caa1970/")

# Source external R scripts for custom functions
source("Code/Child_Outcomes/child_outcomes_functions.R")
source("Code/rounding.r")

# Load the first-generation analysis dataset
load("Data/first_gen_analysis_data_jepmicro_acs.rda")


Table_B1 <- data.frame()
TabB1_Al <- felm(years_of_schooling ~factor(parent_race) + factor(parent_female)+PI_PC*year_trend+
                   PI_PC*I(year_trend^2) +epop*year + total_transfers*year_trend + 
                   county_pop*year_trend + epop*I(year_trend^2) + 
                   total_transfers*I(year_trend^2) + county_pop*I(year_trend^2) +
                   tmax_fullterm+ tmin_fullterm+prec_fullterm+anyprec_fullterm |state_year+county_factor+year_factor+month+survey_year_by_year | (all_fullterm~nonattainment_infant)  |county_factor, 
                 data=parent_piks_acs%>% filter(year%in%1969:1975, AGE >33 & AGE < 44, real_wages<wage_cap,!is.na(parent_age_min)))

summary(TabB1_Al)
TabB1_Al$N
temp_n<-  rounding_rules_counts(TabB1_Al$N)
temp_ctrl <- signif(mean((parent_piks_acs %>% filter(year%in%1969:1975, AGE >33 & AGE < 44, !is.na(all_fullterm),  real_wages<wage_cap, !is.na(PI_PC),nonattainment_infant == 0))$years_of_schooling, na.rm=T ),4)
temp_f <-  signif(TabB1_Al$stage1$iv1fstat[[1]]["F"],4)
temp_results <- tidy(TabB1_Al) %>% filter(term == "`all_fullterm(fit)`" ) %>% #tidy regression, keep coef of interest
  mutate_each(funs(signif(.,4)), -term) %>% #round appropriately
  mutate(N=temp_n, ctrl_mean=temp_ctrl, fstat = temp_f,  table = "B1", column ="A1" ) #add variables for N and control mean, plus index the table number and column
Table_B1 <- bind_rows(Table_B1, temp_results) #Appended to big dataset

TabB1_A2 <- felm(some_college ~factor(parent_race) + factor(parent_female)+PI_PC*year_trend+
                   PI_PC*I(year_trend^2) +epop*year + total_transfers*year_trend + 
                   county_pop*year_trend + epop*I(year_trend^2) + 
                   total_transfers*I(year_trend^2) + county_pop*I(year_trend^2) +
                   tmax_fullterm+ tmin_fullterm+prec_fullterm+anyprec_fullterm |state_year+county_factor+year_factor+month+survey_year_by_year | (all_fullterm~nonattainment_infant)  |county_factor, 
                 data=parent_piks_acs%>% filter(year%in%1969:1975, AGE >33 & AGE < 44, real_wages<wage_cap,!is.na(parent_age_min)))

summary(TabB1_A2)
TabB1_A2$N
temp_n<-  rounding_rules_counts(TabB1_A2$N)
temp_ctrl <- signif(mean((parent_piks_acs %>% filter(year%in%1969:1975, AGE >33 & AGE < 44, !is.na(all_fullterm),  real_wages<wage_cap, !is.na(PI_PC),nonattainment_infant == 0))$some_college, na.rm=T ),4)
temp_f <-  signif(TabB1_A2$stage1$iv1fstat[[1]]["F"],4)
temp_results <- tidy(TabB1_A2) %>% filter(term == "`all_fullterm(fit)`" ) %>% #tidy regression, keep coef of interest
  mutate_each(funs(signif(.,4)), -term) %>% #round appropriately
  mutate(N=temp_n, ctrl_mean=temp_ctrl, fstat = temp_f,  table = "B1", column ="A2" ) #add variables for N and control mean, plus index the table number and column
Table_B1 <- bind_rows(Table_B1, temp_results) #Appended to big dataset

TabB1_A3 <- felm(HS_grad ~factor(parent_race) + factor(parent_female)+PI_PC*year_trend+
                   PI_PC*I(year_trend^2) +epop*year + total_transfers*year_trend + 
                   county_pop*year_trend + epop*I(year_trend^2) + 
                   total_transfers*I(year_trend^2) + county_pop*I(year_trend^2) +
                   tmax_fullterm+ tmin_fullterm+prec_fullterm+anyprec_fullterm |state_year+county_factor+year_factor+month+survey_year_by_year | (all_fullterm~nonattainment_infant)  |county_factor, 
                 data=parent_piks_acs%>% filter(year%in%1969:1975, AGE >33 & AGE < 44, real_wages<wage_cap,!is.na(parent_age_min)))

summary(TabB1_A3)
TabB1_A3$N
temp_n<-  rounding_rules_counts(TabB1_A3$N)
temp_ctrl <- signif(mean((parent_piks_acs %>% filter(year%in%1969:1975, AGE >33 & AGE < 44, !is.na(all_fullterm),  real_wages<wage_cap, !is.na(PI_PC),nonattainment_infant == 0))$HS_grad, na.rm=T ),4)
temp_f <-  signif(TabB1_A3$stage1$iv1fstat[[1]]["F"],4)
temp_results <- tidy(TabB1_A3) %>% filter(term == "`all_fullterm(fit)`" ) %>% #tidy regression, keep coef of interest
  mutate_each(funs(signif(.,4)), -term) %>% #round appropriately
  mutate(N=temp_n, ctrl_mean=temp_ctrl, fstat = temp_f,  table = "B1", column ="A3" ) #add variables for N and control mean, plus index the table number and column
Table_B1 <- bind_rows(Table_B1, temp_results) #Appended to big dataset

TabB1_B1 <- felm(years_of_schooling ~factor(parent_race) + factor(parent_female)+PI_PC*year_trend+
                   PI_PC*I(year_trend^2) +epop*year + total_transfers*year_trend + 
                   county_pop*year_trend + epop*I(year_trend^2) + 
                   total_transfers*I(year_trend^2) + county_pop*I(year_trend^2) +
                   tmax_fullterm+ tmin_fullterm+prec_fullterm+anyprec_fullterm + nonattainment_infant|state_year+county_factor+year_factor+month+survey_year_by_year | 0 |county_factor, 
                 data=parent_piks_acs%>% filter(year%in%1969:1975, AGE >33 & AGE < 44, !is.na(all_fullterm),  real_wages<wage_cap,!is.na(parent_age_min)))

summary(TabB1_B1)
TabB1_B1$N
temp_n<-  rounding_rules_counts(TabB1_B1$N)
temp_ctrl <- signif(mean((parent_piks_acs %>% filter(year%in%1969:1975, AGE >33 & AGE < 44, !is.na(all_fullterm),  real_wages<wage_cap, !is.na(PI_PC),nonattainment_infant == 0))$years_of_schooling, na.rm=T ),4)
temp_results <- tidy(TabB1_B1) %>% filter(term == "nonattainment_infant" ) %>% #tidy regression, keep coef of interest
  mutate_each(funs(signif(.,4)), -term) %>% #round appropriately
  mutate(N=temp_n, ctrl_mean=temp_ctrl,   table = "B1", column ="B1" ) #add variables for N and control mean, plus index the table number and column
Table_B1 <- bind_rows(Table_B1, temp_results) #Appended to big dataset

TabB1_B2 <- felm(some_college ~factor(parent_race) + factor(parent_female)+PI_PC*year_trend+
                   PI_PC*I(year_trend^2) +epop*year + total_transfers*year_trend + 
                   county_pop*year_trend + epop*I(year_trend^2) + 
                   total_transfers*I(year_trend^2) + county_pop*I(year_trend^2) +
                   tmax_fullterm+ tmin_fullterm+prec_fullterm+anyprec_fullterm + nonattainment_infant|state_year+county_factor+year_factor+month+survey_year_by_year | 0 |county_factor, 
                 data=parent_piks_acs%>% filter(year%in%1969:1975, AGE >33 & AGE < 44, !is.na(all_fullterm),  real_wages<wage_cap,!is.na(parent_age_min)))

summary(TabB1_B2)
TabB1_B2$N
temp_n<-  rounding_rules_counts(TabB1_B2$N)
temp_ctrl <- signif(mean((parent_piks_acs %>% filter(year%in%1969:1975, AGE >33 & AGE < 44, !is.na(all_fullterm),  real_wages<wage_cap, !is.na(PI_PC),nonattainment_infant == 0))$some_college, na.rm=T ),4)
temp_results <- tidy(TabB1_B2) %>% filter(term == "nonattainment_infant" ) %>% #tidy regression, keep coef of interest
  mutate_each(funs(signif(.,4)), -term) %>% #round appropriately
  mutate(N=temp_n, ctrl_mean=temp_ctrl,   table = "B1", column ="B2" ) #add variables for N and control mean, plus index the table number and column
Table_B1 <- bind_rows(Table_B1, temp_results) #Appended to big dataset

TabB1_B3 <- felm(HS_grad ~factor(parent_race) + factor(parent_female)+PI_PC*year_trend+
                   PI_PC*I(year_trend^2) +epop*year + total_transfers*year_trend + 
                   county_pop*year_trend + epop*I(year_trend^2) + 
                   total_transfers*I(year_trend^2) + county_pop*I(year_trend^2) +
                   tmax_fullterm+ tmin_fullterm+prec_fullterm+anyprec_fullterm + nonattainment_infant |state_year+county_factor+year_factor+month+survey_year_by_year | 0  |county_factor, 
                 data=parent_piks_acs%>% filter(year%in%1969:1975, AGE >33 & AGE < 44,  real_wages<wage_cap,!is.na(all_fullterm),!is.na(parent_age_min)))

summary(TabB1_B3)
TabB1_B3$N
temp_n<-  rounding_rules_counts(TabB1_B3$N)
temp_ctrl <- signif(mean((parent_piks_acs %>% filter(year%in%1969:1975, AGE >33 & AGE < 44, !is.na(all_fullterm),  real_wages<wage_cap, !is.na(PI_PC),nonattainment_infant == 0))$HS_grad, na.rm=T ),4)
temp_results <- tidy(TabB1_B3) %>% filter(term == "nonattainment_infant" ) %>% #tidy regression, keep coef of interest
  mutate_each(funs(signif(.,4)), -term) %>% #round appropriately
  mutate(N=temp_n, ctrl_mean=temp_ctrl, table = "B1", column ="B3" ) #add variables for N and control mean, plus index the table number and column
Table_B1 <- bind_rows(Table_B1, temp_results) #Appended to big dataset
write_csv(Table_B1, "/projects/opp_env/caa1970/JPE_Micro/Output/Table B1/Table_B1.csv")